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Showing 1 to 20 of 161 for “"mixture models"”.
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Semiparametric mixture models
… three parts that are related to semiparametric mixture models. In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component …
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Prediction with Mixture Models
… to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent, and has to be inferred when learning the mixture model. …
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Algorithms for Mixture Models
Mixture models form one of the most fundamental classes of generative models for clustered data. Specific application examples include text classification problems, image segmentation and motion detection, collaborative filtering and many others. However, quite surprisingly, very little had been …
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Efficient algorithms for learning mixture models
… learning problems for a class of probabilistic models called mixture models. Mixture models are usually used to model settings where the observed data consists of different sub-populations, yet we only have access to a limited number of samples of the pooled data. It includes many widely used …
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On neural spike sorting with mixture models
… attempts to develop a new set of statistical mixture models and methods and apply them to the neural data analysis. The problem we are trying to solve is called neural spike sorting in literature. There are three basic objectives of spike sorting. The first is to estimate the number of neurons …
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Application of the EM Algorithm for Mixture Models
… (EM) algorithm to fit semi-parametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algo rithms (e.g. SAS procedure PROC TRAJ) and standard EM, as well as two other EM-based algorithms for speeding up …
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Handling of Missing Data with Growth Mixture Models
The recent growth of applications of growth mixture models for inference with longitudinal data has introduced a wide range of research dedicated to testing the different aspects of the model. One area of research that has not drawn much attention, however, is the performance of growth mixture …
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Nonparametric Bayesian Quantile Regression via Dirichlet Process Mixture Models
… to quantile regression usingDirichlet process mixture (DPM) models. All the existing quantile regression methodsbased on DPMs require the kernel density to satisfy the quantile constraint, hence thekernel densities are themselves usually in the form of mixtures. One innovation of ourapproaches …
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Statistical Inferences for Two-Component Semiparametric Location-Scale Mixture Models
Mixture models serve as a powerful statistical tool, particularly in capturing heterogeneous populations by representing them as a mixture of several distributions. These models are particularly useful in various fields, including genomics, economics, and social sciences, where data often arises …
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ESTIMATING UNKNOWN KNOTS IN PIECEWISE LINEAR-LINEAR LATENT GROWTH MIXTURE MODELS
A piecewise linear-linear latent growth mixture model (LGMM) combines features of a piecewise linear-linear latent growth curve (LGC) model with the ideas of latent class methods all within a structural equation modeling (SEM) context. A piecewise linear-linear LGMM is an appropriate framework for …
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Layout-aware mixture models for patch-based image representation and analysis
… comprised of millions of pixels, developing models in such a high dimensional space is not always feasible. One of the most popular ways of modeling images is to break them into patches; the reason is that not only is the dimensionality reduced, but it is easier to define similarities between …
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Variational Mixture Models for non-Gaussian observations: Applications to molecular data
… apply variational non-Gaussian Dirichlet Process mixture models because they have infinite number of components that allow model-determination and are flexible to model any discrete or continuous data type. We also employ Variational Inference with the “annealing” extension that accounts for poor …
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One-Stage and Bayesian Two-Stage Optimal Designs for Mixture Models
… Bayesian two-stage D-D optimal designs for mixture experiments with or without process variables under model uncertainty are developed. A Bayesian optimality criterion is used in the first stage to minimize the determinant of the posterior variances of the parameters. The second stage design …
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Normal Mixture Models for Gene Cluster Identification in Two Dimensional Microarray Data
… A novel clustering technique based on normal mixture distribution models is developed. This method clusters observations that arise from the same normal distribution and allows the data to be simultaneously clustered in two dimensions. The model is fitted using the Expectation/Maximization …
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Data assimilation with Gaussian mixture models using the dynamically orthogonal field equations
… of sparse observational data with computational models so as to optimally improve the probabilistic description of the field of interest, thereby reducing uncertainties. The centerpiece of this thesis is the introduction of a novel such scheme that overcomes prior shortcomings observed within the …
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Maximum likelihood parameter estimation of mixture models and its application to image segmentation and restoration
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Improving Computation for Hierarchical Bayesian Spatial Gaussian Mixture Models with Application to the Analysis of THz image of Breast Tumor
… chapter starts with an overview of Gaussian mixture models (GMMs). However, because in the GMM framework the observations are assumed to be independent, GMMs are less effective when the mixture data exhibits spatial autocorrelation. To improve the performance of GMMs on spatially-correlated …
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A framework based on Gaussian mixture models and Kalman filters for the segmentation and tracking of anomalous events in shipboard video
… algorithm based on adaptive Gaussian mixture models is employed to detect the presence of motion in a scene. The algorithm is adapted to emphasize gray-level characteristics related to smoke and fire events in the frame. Next, shape discriminant features in the foreground are enhanced …
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Statistical Models for Gene and Transcripts Quantification and Identification Using RNA-Seq Technology
… study. First, we propose to use finite Poisson mixture models (PMI) to characterize base pair-level RNA-Seq data and further quantify gene expression levels. Finite Poisson mixture models combine the strength of fully parametric models with the flexibility of fully nonparametric models, and are …
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